Development and Validation of an Unsteady State Numerical Model of Fouling within a Crystalline Systern
Bibliographic record
Abstract
Abstract Fouling is a phenomenon that threatens the sustainability of thermal and membrane desalination processes. The deposition of fouling material on the heatlmass transfer surface increases the amount of energy required for operation. Traditional fouling research has focused on experimental investigations that provide limited results and macroscopic assessment of the process. This research uses computational fluid dynamics (CFD) to model the transient nature of fouling and obtains an insight into the intricate interactions of the variables that influence fouling on a local scale. The authors developed a Eulerian model describing both the induction and deposition processes of the crystallisation fouling mechanism. The detail provided by the CFD model demonstrated that scale growth has a considerable impact on the hydrodynamics of the system, and vice‐versa. The intricate relationships between the operating variables affect the hydrodynamics and boundary layer conditions, and impact on both the heat and mass transfer. The deposit growth causes a decrease in the thickness of the mass boundary layer, thus promoting transport towards the growing crystal layer. Validation of this model showed good agreement with experimental data in terms of its ability to predict local fouling behaviour and rates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".